Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Monday 27 July 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 189,033 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 189,023 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 14
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 7
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 8
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
## 117 2020-06-25 East of England 1
## 118 2020-06-26 East of England 5
## 119 2020-06-27 East of England 6
## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 7
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
## 140 2020-07-18 East of England 0
## 141 2020-07-19 East of England 1
## 142 2020-07-20 East of England 1
## 143 2020-07-21 East of England 0
## 144 2020-07-22 East of England 1
## 145 2020-07-23 East of England 1
## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
## 148 2020-03-01 London 0
## 149 2020-03-02 London 0
## 150 2020-03-03 London 0
## 151 2020-03-04 London 0
## 152 2020-03-05 London 0
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## 296 2020-03-02 Midlands 0
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## 302 2020-03-08 Midlands 2
## 303 2020-03-09 Midlands 1
## 304 2020-03-10 Midlands 0
## 305 2020-03-11 Midlands 2
## 306 2020-03-12 Midlands 6
## 307 2020-03-13 Midlands 5
## 308 2020-03-14 Midlands 4
## 309 2020-03-15 Midlands 5
## 310 2020-03-16 Midlands 11
## 311 2020-03-17 Midlands 8
## 312 2020-03-18 Midlands 13
## 313 2020-03-19 Midlands 8
## 314 2020-03-20 Midlands 28
## 315 2020-03-21 Midlands 13
## 316 2020-03-22 Midlands 31
## 317 2020-03-23 Midlands 33
## 318 2020-03-24 Midlands 41
## 319 2020-03-25 Midlands 48
## 320 2020-03-26 Midlands 64
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## 323 2020-03-29 Midlands 92
## 324 2020-03-30 Midlands 90
## 325 2020-03-31 Midlands 123
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## 327 2020-04-02 Midlands 142
## 328 2020-04-03 Midlands 124
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## 345 2020-04-20 Midlands 107
## 346 2020-04-21 Midlands 86
## 347 2020-04-22 Midlands 78
## 348 2020-04-23 Midlands 103
## 349 2020-04-24 Midlands 79
## 350 2020-04-25 Midlands 72
## 351 2020-04-26 Midlands 81
## 352 2020-04-27 Midlands 74
## 353 2020-04-28 Midlands 68
## 354 2020-04-29 Midlands 53
## 355 2020-04-30 Midlands 56
## 356 2020-05-01 Midlands 64
## 357 2020-05-02 Midlands 51
## 358 2020-05-03 Midlands 52
## 359 2020-05-04 Midlands 61
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## 361 2020-05-06 Midlands 59
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## 364 2020-05-09 Midlands 37
## 365 2020-05-10 Midlands 42
## 366 2020-05-11 Midlands 33
## 367 2020-05-12 Midlands 45
## 368 2020-05-13 Midlands 40
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## 370 2020-05-15 Midlands 40
## 371 2020-05-16 Midlands 34
## 372 2020-05-17 Midlands 31
## 373 2020-05-18 Midlands 36
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## 375 2020-05-20 Midlands 36
## 376 2020-05-21 Midlands 32
## 377 2020-05-22 Midlands 27
## 378 2020-05-23 Midlands 34
## 379 2020-05-24 Midlands 20
## 380 2020-05-25 Midlands 26
## 381 2020-05-26 Midlands 33
## 382 2020-05-27 Midlands 29
## 383 2020-05-28 Midlands 28
## 384 2020-05-29 Midlands 20
## 385 2020-05-30 Midlands 21
## 386 2020-05-31 Midlands 22
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## 388 2020-06-02 Midlands 22
## 389 2020-06-03 Midlands 24
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## 391 2020-06-05 Midlands 21
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## 393 2020-06-07 Midlands 17
## 394 2020-06-08 Midlands 16
## 395 2020-06-09 Midlands 18
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## 426 2020-07-10 Midlands 3
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## 429 2020-07-13 Midlands 1
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## 432 2020-07-16 Midlands 2
## 433 2020-07-17 Midlands 2
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## 436 2020-07-20 Midlands 3
## 437 2020-07-21 Midlands 0
## 438 2020-07-22 Midlands 2
## 439 2020-07-23 Midlands 5
## 440 2020-07-24 Midlands 1
## 441 2020-07-25 Midlands 0
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## 443 2020-03-02 North East and Yorkshire 0
## 444 2020-03-03 North East and Yorkshire 0
## 445 2020-03-04 North East and Yorkshire 0
## 446 2020-03-05 North East and Yorkshire 0
## 447 2020-03-06 North East and Yorkshire 0
## 448 2020-03-07 North East and Yorkshire 0
## 449 2020-03-08 North East and Yorkshire 0
## 450 2020-03-09 North East and Yorkshire 0
## 451 2020-03-10 North East and Yorkshire 0
## 452 2020-03-11 North East and Yorkshire 0
## 453 2020-03-12 North East and Yorkshire 0
## 454 2020-03-13 North East and Yorkshire 0
## 455 2020-03-14 North East and Yorkshire 0
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## 457 2020-03-16 North East and Yorkshire 3
## 458 2020-03-17 North East and Yorkshire 1
## 459 2020-03-18 North East and Yorkshire 2
## 460 2020-03-19 North East and Yorkshire 6
## 461 2020-03-20 North East and Yorkshire 5
## 462 2020-03-21 North East and Yorkshire 6
## 463 2020-03-22 North East and Yorkshire 7
## 464 2020-03-23 North East and Yorkshire 9
## 465 2020-03-24 North East and Yorkshire 8
## 466 2020-03-25 North East and Yorkshire 18
## 467 2020-03-26 North East and Yorkshire 21
## 468 2020-03-27 North East and Yorkshire 28
## 469 2020-03-28 North East and Yorkshire 35
## 470 2020-03-29 North East and Yorkshire 38
## 471 2020-03-30 North East and Yorkshire 64
## 472 2020-03-31 North East and Yorkshire 60
## 473 2020-04-01 North East and Yorkshire 67
## 474 2020-04-02 North East and Yorkshire 75
## 475 2020-04-03 North East and Yorkshire 100
## 476 2020-04-04 North East and Yorkshire 105
## 477 2020-04-05 North East and Yorkshire 92
## 478 2020-04-06 North East and Yorkshire 96
## 479 2020-04-07 North East and Yorkshire 102
## 480 2020-04-08 North East and Yorkshire 107
## 481 2020-04-09 North East and Yorkshire 111
## 482 2020-04-10 North East and Yorkshire 117
## 483 2020-04-11 North East and Yorkshire 98
## 484 2020-04-12 North East and Yorkshire 84
## 485 2020-04-13 North East and Yorkshire 94
## 486 2020-04-14 North East and Yorkshire 107
## 487 2020-04-15 North East and Yorkshire 96
## 488 2020-04-16 North East and Yorkshire 103
## 489 2020-04-17 North East and Yorkshire 88
## 490 2020-04-18 North East and Yorkshire 95
## 491 2020-04-19 North East and Yorkshire 88
## 492 2020-04-20 North East and Yorkshire 100
## 493 2020-04-21 North East and Yorkshire 76
## 494 2020-04-22 North East and Yorkshire 84
## 495 2020-04-23 North East and Yorkshire 63
## 496 2020-04-24 North East and Yorkshire 72
## 497 2020-04-25 North East and Yorkshire 69
## 498 2020-04-26 North East and Yorkshire 65
## 499 2020-04-27 North East and Yorkshire 65
## 500 2020-04-28 North East and Yorkshire 57
## 501 2020-04-29 North East and Yorkshire 69
## 502 2020-04-30 North East and Yorkshire 57
## 503 2020-05-01 North East and Yorkshire 64
## 504 2020-05-02 North East and Yorkshire 48
## 505 2020-05-03 North East and Yorkshire 40
## 506 2020-05-04 North East and Yorkshire 49
## 507 2020-05-05 North East and Yorkshire 40
## 508 2020-05-06 North East and Yorkshire 51
## 509 2020-05-07 North East and Yorkshire 45
## 510 2020-05-08 North East and Yorkshire 42
## 511 2020-05-09 North East and Yorkshire 44
## 512 2020-05-10 North East and Yorkshire 40
## 513 2020-05-11 North East and Yorkshire 29
## 514 2020-05-12 North East and Yorkshire 27
## 515 2020-05-13 North East and Yorkshire 28
## 516 2020-05-14 North East and Yorkshire 31
## 517 2020-05-15 North East and Yorkshire 32
## 518 2020-05-16 North East and Yorkshire 35
## 519 2020-05-17 North East and Yorkshire 26
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## 530 2020-05-28 North East and Yorkshire 21
## 531 2020-05-29 North East and Yorkshire 25
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## 533 2020-05-31 North East and Yorkshire 20
## 534 2020-06-01 North East and Yorkshire 17
## 535 2020-06-02 North East and Yorkshire 23
## 536 2020-06-03 North East and Yorkshire 23
## 537 2020-06-04 North East and Yorkshire 17
## 538 2020-06-05 North East and Yorkshire 18
## 539 2020-06-06 North East and Yorkshire 21
## 540 2020-06-07 North East and Yorkshire 14
## 541 2020-06-08 North East and Yorkshire 11
## 542 2020-06-09 North East and Yorkshire 12
## 543 2020-06-10 North East and Yorkshire 19
## 544 2020-06-11 North East and Yorkshire 7
## 545 2020-06-12 North East and Yorkshire 9
## 546 2020-06-13 North East and Yorkshire 10
## 547 2020-06-14 North East and Yorkshire 11
## 548 2020-06-15 North East and Yorkshire 9
## 549 2020-06-16 North East and Yorkshire 10
## 550 2020-06-17 North East and Yorkshire 9
## 551 2020-06-18 North East and Yorkshire 11
## 552 2020-06-19 North East and Yorkshire 6
## 553 2020-06-20 North East and Yorkshire 5
## 554 2020-06-21 North East and Yorkshire 4
## 555 2020-06-22 North East and Yorkshire 7
## 556 2020-06-23 North East and Yorkshire 7
## 557 2020-06-24 North East and Yorkshire 10
## 558 2020-06-25 North East and Yorkshire 4
## 559 2020-06-26 North East and Yorkshire 7
## 560 2020-06-27 North East and Yorkshire 3
## 561 2020-06-28 North East and Yorkshire 5
## 562 2020-06-29 North East and Yorkshire 2
## 563 2020-06-30 North East and Yorkshire 6
## 564 2020-07-01 North East and Yorkshire 1
## 565 2020-07-02 North East and Yorkshire 4
## 566 2020-07-03 North East and Yorkshire 4
## 567 2020-07-04 North East and Yorkshire 4
## 568 2020-07-05 North East and Yorkshire 2
## 569 2020-07-06 North East and Yorkshire 2
## 570 2020-07-07 North East and Yorkshire 3
## 571 2020-07-08 North East and Yorkshire 3
## 572 2020-07-09 North East and Yorkshire 0
## 573 2020-07-10 North East and Yorkshire 3
## 574 2020-07-11 North East and Yorkshire 1
## 575 2020-07-12 North East and Yorkshire 4
## 576 2020-07-13 North East and Yorkshire 1
## 577 2020-07-14 North East and Yorkshire 1
## 578 2020-07-15 North East and Yorkshire 2
## 579 2020-07-16 North East and Yorkshire 2
## 580 2020-07-17 North East and Yorkshire 1
## 581 2020-07-18 North East and Yorkshire 2
## 582 2020-07-19 North East and Yorkshire 2
## 583 2020-07-20 North East and Yorkshire 1
## 584 2020-07-21 North East and Yorkshire 1
## 585 2020-07-22 North East and Yorkshire 4
## 586 2020-07-23 North East and Yorkshire 0
## 587 2020-07-24 North East and Yorkshire 0
## 588 2020-07-25 North East and Yorkshire 1
## 589 2020-03-01 North West 0
## 590 2020-03-02 North West 0
## 591 2020-03-03 North West 0
## 592 2020-03-04 North West 0
## 593 2020-03-05 North West 1
## 594 2020-03-06 North West 0
## 595 2020-03-07 North West 0
## 596 2020-03-08 North West 1
## 597 2020-03-09 North West 0
## 598 2020-03-10 North West 0
## 599 2020-03-11 North West 0
## 600 2020-03-12 North West 2
## 601 2020-03-13 North West 3
## 602 2020-03-14 North West 1
## 603 2020-03-15 North West 4
## 604 2020-03-16 North West 2
## 605 2020-03-17 North West 4
## 606 2020-03-18 North West 6
## 607 2020-03-19 North West 7
## 608 2020-03-20 North West 10
## 609 2020-03-21 North West 11
## 610 2020-03-22 North West 13
## 611 2020-03-23 North West 15
## 612 2020-03-24 North West 21
## 613 2020-03-25 North West 21
## 614 2020-03-26 North West 29
## 615 2020-03-27 North West 36
## 616 2020-03-28 North West 28
## 617 2020-03-29 North West 46
## 618 2020-03-30 North West 67
## 619 2020-03-31 North West 52
## 620 2020-04-01 North West 86
## 621 2020-04-02 North West 96
## 622 2020-04-03 North West 95
## 623 2020-04-04 North West 98
## 624 2020-04-05 North West 102
## 625 2020-04-06 North West 100
## 626 2020-04-07 North West 135
## 627 2020-04-08 North West 127
## 628 2020-04-09 North West 119
## 629 2020-04-10 North West 117
## 630 2020-04-11 North West 138
## 631 2020-04-12 North West 125
## 632 2020-04-13 North West 129
## 633 2020-04-14 North West 131
## 634 2020-04-15 North West 114
## 635 2020-04-16 North West 135
## 636 2020-04-17 North West 98
## 637 2020-04-18 North West 113
## 638 2020-04-19 North West 71
## 639 2020-04-20 North West 83
## 640 2020-04-21 North West 76
## 641 2020-04-22 North West 86
## 642 2020-04-23 North West 85
## 643 2020-04-24 North West 66
## 644 2020-04-25 North West 66
## 645 2020-04-26 North West 55
## 646 2020-04-27 North West 54
## 647 2020-04-28 North West 57
## 648 2020-04-29 North West 63
## 649 2020-04-30 North West 59
## 650 2020-05-01 North West 45
## 651 2020-05-02 North West 56
## 652 2020-05-03 North West 55
## 653 2020-05-04 North West 48
## 654 2020-05-05 North West 48
## 655 2020-05-06 North West 44
## 656 2020-05-07 North West 49
## 657 2020-05-08 North West 42
## 658 2020-05-09 North West 31
## 659 2020-05-10 North West 42
## 660 2020-05-11 North West 35
## 661 2020-05-12 North West 38
## 662 2020-05-13 North West 25
## 663 2020-05-14 North West 26
## 664 2020-05-15 North West 33
## 665 2020-05-16 North West 32
## 666 2020-05-17 North West 24
## 667 2020-05-18 North West 31
## 668 2020-05-19 North West 35
## 669 2020-05-20 North West 27
## 670 2020-05-21 North West 27
## 671 2020-05-22 North West 26
## 672 2020-05-23 North West 31
## 673 2020-05-24 North West 26
## 674 2020-05-25 North West 31
## 675 2020-05-26 North West 27
## 676 2020-05-27 North West 27
## 677 2020-05-28 North West 28
## 678 2020-05-29 North West 20
## 679 2020-05-30 North West 19
## 680 2020-05-31 North West 13
## 681 2020-06-01 North West 12
## 682 2020-06-02 North West 27
## 683 2020-06-03 North West 22
## 684 2020-06-04 North West 22
## 685 2020-06-05 North West 16
## 686 2020-06-06 North West 26
## 687 2020-06-07 North West 20
## 688 2020-06-08 North West 23
## 689 2020-06-09 North West 17
## 690 2020-06-10 North West 16
## 691 2020-06-11 North West 16
## 692 2020-06-12 North West 11
## 693 2020-06-13 North West 10
## 694 2020-06-14 North West 15
## 695 2020-06-15 North West 16
## 696 2020-06-16 North West 15
## 697 2020-06-17 North West 13
## 698 2020-06-18 North West 14
## 699 2020-06-19 North West 7
## 700 2020-06-20 North West 11
## 701 2020-06-21 North West 8
## 702 2020-06-22 North West 11
## 703 2020-06-23 North West 13
## 704 2020-06-24 North West 13
## 705 2020-06-25 North West 15
## 706 2020-06-26 North West 6
## 707 2020-06-27 North West 7
## 708 2020-06-28 North West 9
## 709 2020-06-29 North West 9
## 710 2020-06-30 North West 7
## 711 2020-07-01 North West 3
## 712 2020-07-02 North West 6
## 713 2020-07-03 North West 7
## 714 2020-07-04 North West 4
## 715 2020-07-05 North West 6
## 716 2020-07-06 North West 9
## 717 2020-07-07 North West 8
## 718 2020-07-08 North West 5
## 719 2020-07-09 North West 10
## 720 2020-07-10 North West 2
## 721 2020-07-11 North West 4
## 722 2020-07-12 North West 0
## 723 2020-07-13 North West 6
## 724 2020-07-14 North West 4
## 725 2020-07-15 North West 5
## 726 2020-07-16 North West 2
## 727 2020-07-17 North West 4
## 728 2020-07-18 North West 4
## 729 2020-07-19 North West 2
## 730 2020-07-20 North West 0
## 731 2020-07-21 North West 1
## 732 2020-07-22 North West 0
## 733 2020-07-23 North West 1
## 734 2020-07-24 North West 0
## 735 2020-07-25 North West 0
## 736 2020-03-01 South East 0
## 737 2020-03-02 South East 0
## 738 2020-03-03 South East 1
## 739 2020-03-04 South East 0
## 740 2020-03-05 South East 1
## 741 2020-03-06 South East 0
## 742 2020-03-07 South East 0
## 743 2020-03-08 South East 1
## 744 2020-03-09 South East 1
## 745 2020-03-10 South East 1
## 746 2020-03-11 South East 1
## 747 2020-03-12 South East 0
## 748 2020-03-13 South East 1
## 749 2020-03-14 South East 1
## 750 2020-03-15 South East 5
## 751 2020-03-16 South East 8
## 752 2020-03-17 South East 7
## 753 2020-03-18 South East 10
## 754 2020-03-19 South East 9
## 755 2020-03-20 South East 13
## 756 2020-03-21 South East 7
## 757 2020-03-22 South East 25
## 758 2020-03-23 South East 20
## 759 2020-03-24 South East 22
## 760 2020-03-25 South East 29
## 761 2020-03-26 South East 35
## 762 2020-03-27 South East 34
## 763 2020-03-28 South East 36
## 764 2020-03-29 South East 55
## 765 2020-03-30 South East 58
## 766 2020-03-31 South East 65
## 767 2020-04-01 South East 66
## 768 2020-04-02 South East 55
## 769 2020-04-03 South East 72
## 770 2020-04-04 South East 80
## 771 2020-04-05 South East 82
## 772 2020-04-06 South East 88
## 773 2020-04-07 South East 100
## 774 2020-04-08 South East 83
## 775 2020-04-09 South East 104
## 776 2020-04-10 South East 88
## 777 2020-04-11 South East 88
## 778 2020-04-12 South East 88
## 779 2020-04-13 South East 84
## 780 2020-04-14 South East 65
## 781 2020-04-15 South East 72
## 782 2020-04-16 South East 56
## 783 2020-04-17 South East 86
## 784 2020-04-18 South East 57
## 785 2020-04-19 South East 70
## 786 2020-04-20 South East 87
## 787 2020-04-21 South East 51
## 788 2020-04-22 South East 54
## 789 2020-04-23 South East 57
## 790 2020-04-24 South East 64
## 791 2020-04-25 South East 51
## 792 2020-04-26 South East 51
## 793 2020-04-27 South East 41
## 794 2020-04-28 South East 40
## 795 2020-04-29 South East 47
## 796 2020-04-30 South East 29
## 797 2020-05-01 South East 37
## 798 2020-05-02 South East 36
## 799 2020-05-03 South East 17
## 800 2020-05-04 South East 35
## 801 2020-05-05 South East 29
## 802 2020-05-06 South East 25
## 803 2020-05-07 South East 27
## 804 2020-05-08 South East 26
## 805 2020-05-09 South East 28
## 806 2020-05-10 South East 19
## 807 2020-05-11 South East 25
## 808 2020-05-12 South East 27
## 809 2020-05-13 South East 18
## 810 2020-05-14 South East 32
## 811 2020-05-15 South East 25
## 812 2020-05-16 South East 22
## 813 2020-05-17 South East 18
## 814 2020-05-18 South East 22
## 815 2020-05-19 South East 12
## 816 2020-05-20 South East 22
## 817 2020-05-21 South East 15
## 818 2020-05-22 South East 17
## 819 2020-05-23 South East 21
## 820 2020-05-24 South East 17
## 821 2020-05-25 South East 13
## 822 2020-05-26 South East 19
## 823 2020-05-27 South East 18
## 824 2020-05-28 South East 12
## 825 2020-05-29 South East 21
## 826 2020-05-30 South East 8
## 827 2020-05-31 South East 12
## 828 2020-06-01 South East 11
## 829 2020-06-02 South East 13
## 830 2020-06-03 South East 18
## 831 2020-06-04 South East 11
## 832 2020-06-05 South East 11
## 833 2020-06-06 South East 10
## 834 2020-06-07 South East 12
## 835 2020-06-08 South East 8
## 836 2020-06-09 South East 10
## 837 2020-06-10 South East 11
## 838 2020-06-11 South East 5
## 839 2020-06-12 South East 6
## 840 2020-06-13 South East 7
## 841 2020-06-14 South East 7
## 842 2020-06-15 South East 8
## 843 2020-06-16 South East 14
## 844 2020-06-17 South East 9
## 845 2020-06-18 South East 4
## 846 2020-06-19 South East 7
## 847 2020-06-20 South East 5
## 848 2020-06-21 South East 3
## 849 2020-06-22 South East 2
## 850 2020-06-23 South East 8
## 851 2020-06-24 South East 7
## 852 2020-06-25 South East 5
## 853 2020-06-26 South East 8
## 854 2020-06-27 South East 9
## 855 2020-06-28 South East 6
## 856 2020-06-29 South East 5
## 857 2020-06-30 South East 5
## 858 2020-07-01 South East 2
## 859 2020-07-02 South East 7
## 860 2020-07-03 South East 3
## 861 2020-07-04 South East 6
## 862 2020-07-05 South East 4
## 863 2020-07-06 South East 4
## 864 2020-07-07 South East 6
## 865 2020-07-08 South East 3
## 866 2020-07-09 South East 7
## 867 2020-07-10 South East 3
## 868 2020-07-11 South East 3
## 869 2020-07-12 South East 4
## 870 2020-07-13 South East 4
## 871 2020-07-14 South East 5
## 872 2020-07-15 South East 6
## 873 2020-07-16 South East 3
## 874 2020-07-17 South East 1
## 875 2020-07-18 South East 4
## 876 2020-07-19 South East 2
## 877 2020-07-20 South East 3
## 878 2020-07-21 South East 4
## 879 2020-07-22 South East 2
## 880 2020-07-23 South East 2
## 881 2020-07-24 South East 1
## 882 2020-07-25 South East 0
## 883 2020-03-01 South West 0
## 884 2020-03-02 South West 0
## 885 2020-03-03 South West 0
## 886 2020-03-04 South West 0
## 887 2020-03-05 South West 0
## 888 2020-03-06 South West 0
## 889 2020-03-07 South West 0
## 890 2020-03-08 South West 0
## 891 2020-03-09 South West 0
## 892 2020-03-10 South West 0
## 893 2020-03-11 South West 1
## 894 2020-03-12 South West 0
## 895 2020-03-13 South West 0
## 896 2020-03-14 South West 1
## 897 2020-03-15 South West 0
## 898 2020-03-16 South West 0
## 899 2020-03-17 South West 2
## 900 2020-03-18 South West 2
## 901 2020-03-19 South West 4
## 902 2020-03-20 South West 3
## 903 2020-03-21 South West 6
## 904 2020-03-22 South West 7
## 905 2020-03-23 South West 8
## 906 2020-03-24 South West 7
## 907 2020-03-25 South West 9
## 908 2020-03-26 South West 11
## 909 2020-03-27 South West 13
## 910 2020-03-28 South West 21
## 911 2020-03-29 South West 18
## 912 2020-03-30 South West 23
## 913 2020-03-31 South West 23
## 914 2020-04-01 South West 21
## 915 2020-04-02 South West 23
## 916 2020-04-03 South West 30
## 917 2020-04-04 South West 42
## 918 2020-04-05 South West 32
## 919 2020-04-06 South West 34
## 920 2020-04-07 South West 39
## 921 2020-04-08 South West 47
## 922 2020-04-09 South West 24
## 923 2020-04-10 South West 46
## 924 2020-04-11 South West 43
## 925 2020-04-12 South West 23
## 926 2020-04-13 South West 27
## 927 2020-04-14 South West 24
## 928 2020-04-15 South West 32
## 929 2020-04-16 South West 29
## 930 2020-04-17 South West 33
## 931 2020-04-18 South West 25
## 932 2020-04-19 South West 31
## 933 2020-04-20 South West 26
## 934 2020-04-21 South West 26
## 935 2020-04-22 South West 23
## 936 2020-04-23 South West 17
## 937 2020-04-24 South West 19
## 938 2020-04-25 South West 15
## 939 2020-04-26 South West 27
## 940 2020-04-27 South West 13
## 941 2020-04-28 South West 17
## 942 2020-04-29 South West 15
## 943 2020-04-30 South West 26
## 944 2020-05-01 South West 6
## 945 2020-05-02 South West 7
## 946 2020-05-03 South West 10
## 947 2020-05-04 South West 17
## 948 2020-05-05 South West 14
## 949 2020-05-06 South West 19
## 950 2020-05-07 South West 16
## 951 2020-05-08 South West 6
## 952 2020-05-09 South West 11
## 953 2020-05-10 South West 5
## 954 2020-05-11 South West 8
## 955 2020-05-12 South West 7
## 956 2020-05-13 South West 7
## 957 2020-05-14 South West 6
## 958 2020-05-15 South West 4
## 959 2020-05-16 South West 4
## 960 2020-05-17 South West 6
## 961 2020-05-18 South West 4
## 962 2020-05-19 South West 6
## 963 2020-05-20 South West 1
## 964 2020-05-21 South West 9
## 965 2020-05-22 South West 6
## 966 2020-05-23 South West 6
## 967 2020-05-24 South West 3
## 968 2020-05-25 South West 8
## 969 2020-05-26 South West 11
## 970 2020-05-27 South West 5
## 971 2020-05-28 South West 10
## 972 2020-05-29 South West 7
## 973 2020-05-30 South West 3
## 974 2020-05-31 South West 2
## 975 2020-06-01 South West 7
## 976 2020-06-02 South West 2
## 977 2020-06-03 South West 7
## 978 2020-06-04 South West 2
## 979 2020-06-05 South West 2
## 980 2020-06-06 South West 1
## 981 2020-06-07 South West 3
## 982 2020-06-08 South West 3
## 983 2020-06-09 South West 0
## 984 2020-06-10 South West 1
## 985 2020-06-11 South West 2
## 986 2020-06-12 South West 2
## 987 2020-06-13 South West 2
## 988 2020-06-14 South West 0
## 989 2020-06-15 South West 2
## 990 2020-06-16 South West 2
## 991 2020-06-17 South West 0
## 992 2020-06-18 South West 0
## 993 2020-06-19 South West 0
## 994 2020-06-20 South West 2
## 995 2020-06-21 South West 0
## 996 2020-06-22 South West 1
## 997 2020-06-23 South West 1
## 998 2020-06-24 South West 1
## 999 2020-06-25 South West 0
## 1000 2020-06-26 South West 3
## 1001 2020-06-27 South West 0
## 1002 2020-06-28 South West 0
## 1003 2020-06-29 South West 1
## 1004 2020-06-30 South West 0
## 1005 2020-07-01 South West 0
## 1006 2020-07-02 South West 0
## 1007 2020-07-03 South West 0
## 1008 2020-07-04 South West 0
## 1009 2020-07-05 South West 1
## 1010 2020-07-06 South West 0
## 1011 2020-07-07 South West 0
## 1012 2020-07-08 South West 2
## 1013 2020-07-09 South West 0
## 1014 2020-07-10 South West 1
## 1015 2020-07-11 South West 0
## 1016 2020-07-12 South West 0
## 1017 2020-07-13 South West 1
## 1018 2020-07-14 South West 0
## 1019 2020-07-15 South West 0
## 1020 2020-07-16 South West 0
## 1021 2020-07-17 South West 1
## 1022 2020-07-18 South West 0
## 1023 2020-07-19 South West 0
## 1024 2020-07-20 South West 0
## 1025 2020-07-21 South West 0
## 1026 2020-07-22 South West 0
## 1027 2020-07-23 South West 0
## 1028 2020-07-24 South West 0
## 1029 2020-07-25 South West 01.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Sunday 26 Jul 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -14.5216 -5.2178 -0.5809 3.9109 9.2908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.485e+00 6.918e-02 64.83 <2e-16 ***
## note_lag 1.545e-05 7.223e-07 21.39 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 29.28938)
##
## Null deviance: 14715.4 on 85 degrees of freedom
## Residual deviance: 2642.8 on 84 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 88.673079 1.000015
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 77.246008 101.319185
## note_lag 1.000014 1.000017
Rsq(lag_mod)
## [1] 0.820408
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.2.0
## [10] projections_0.5.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.2 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.3 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 fs_1.4.2 webshot_0.5.2 httr_1.4.2
## [5] rprojroot_1.3-2 tools_4.0.2 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 mgcv_1.8-31 DBI_1.1.0 colorspace_1.4-1
## [13] withr_2.2.0 gridExtra_2.3 tidyselect_1.1.0 sodium_1.1
## [17] curl_4.3 compiler_4.0.2 cli_2.0.2 labeling_0.3
## [21] matchmaker_0.1.1 scales_1.1.1 digest_0.6.25 foreign_0.8-80
## [25] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
## [29] htmlwidgets_1.5.1 rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
## [33] farver_2.0.3 generics_0.0.2 jsonlite_1.7.0 crosstalk_1.1.0.1
## [37] car_3.0-8 zip_2.0.4 magrittr_1.5 kyotil_2019.11-22
## [41] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0 fansi_0.4.1
## [45] viridis_0.5.1 abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [49] yaml_2.2.1 carData_3.0-4 snakecase_0.11.0 MASS_7.3-51.6
## [53] plyr_1.8.6 grid_4.0.2 blob_1.2.1 crayon_1.3.4
## [57] lattice_0.20-41 cowplot_1.0.0 splines_4.0.2 haven_2.3.1
## [61] hms_0.5.3 knitr_1.29 pillar_1.4.6 boot_1.3-25
## [65] ggsignif_0.6.0 reprex_0.3.0 glue_1.4.1 evaluate_0.14
## [69] data.table_1.13.0 modelr_0.1.8 vctrs_0.3.2 selectr_0.4-2
## [73] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1 xfun_0.16
## [77] openxlsx_4.1.5 broom_0.7.0 rstatix_0.6.0 survival_3.1-12
## [81] viridisLite_0.3.0 ellipsis_0.3.1